{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验六：KMeans聚类算法\n",
    "\n",
    "### 罗玉轩 20221202433\n",
    "\n",
    "## 一、实验目的\n",
    "\n",
    "1. 实现基于欧式距离的KMeans聚类算法\n",
    "2. 在iris数据集上应用KMeans算法\n",
    "3. 可视化聚类结果\n",
    "4. 计算聚类评估指标：F-measure, ACC, NMI, RI, ARI\n",
    "\n",
    "## 二、实验环境\n",
    "\n",
    "- Python 3.9+\n",
    "- Jupyter Notebook\n",
    "- 所需库：numpy, pandas, matplotlib, scikit-learn\n",
    "\n",
    "## 三、实验内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入所需库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    normalized_mutual_info_score,\n",
    "    adjusted_rand_score,\n",
    "    f1_score\n",
    ")\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "data = pd.read_csv('src/iris.csv')\n",
    "X = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, -1].values\n",
    "\n",
    "# 编码标签\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KMeans实现\n",
    "class KMeans:\n",
    "    def __init__(self, n_clusters=3, max_iter=300, tol=1e-4, random_state=None):\n",
    "        self.n_clusters = n_clusters\n",
    "        self.max_iter = max_iter\n",
    "        self.tol = tol\n",
    "        self.random_state = random_state\n",
    "        self.centroids = None\n",
    "        self.labels_ = None\n",
    "        self.inertia_ = None\n",
    "    \n",
    "    def fit(self, X):\n",
    "        if self.random_state is not None:\n",
    "            np.random.seed(self.random_state)\n",
    "            \n",
    "        # 随机初始化质心\n",
    "        n_samples = X.shape[0]\n",
    "        random_indices = np.random.choice(n_samples, self.n_clusters, replace=False)\n",
    "        self.centroids = X[random_indices]\n",
    "        \n",
    "        for i in range(self.max_iter):\n",
    "            # 分配样本到最近的质心\n",
    "            distances = self._calc_distances(X)\n",
    "            labels = np.argmin(distances, axis=1)\n",
    "            \n",
    "            # 计算新的质心\n",
    "            new_centroids = np.array([X[labels == k].mean(axis=0) \n",
    "                                    for k in range(self.n_clusters)])\n",
    "            \n",
    "            # 检查收敛\n",
    "            if np.allclose(self.centroids, new_centroids, atol=self.tol):\n",
    "                break\n",
    "                \n",
    "            self.centroids = new_centroids\n",
    "        \n",
    "        self.labels_ = labels\n",
    "        self.inertia_ = np.sum(np.min(distances, axis=1))\n",
    "        \n",
    "    def _calc_distances(self, X):\n",
    "        return np.array([np.sum((X - centroid)**2, axis=1) \n",
    "                        for centroid in self.centroids]).T\n",
    "\n",
    "    def predict(self, X):\n",
    "        distances = self._calc_distances(X)\n",
    "        return np.argmin(distances, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 运行KMeans\n",
    "kmeans = KMeans(n_clusters=3, max_iter=300, random_state=42)\n",
    "kmeans.fit(X)\n",
    "y_pred = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "聚类评估指标:\n",
      "Accuracy: 0.2400\n",
      "NMI: 0.7582\n",
      "ARI: 0.7302\n",
      "F_measure: 0.2727\n"
     ]
    }
   ],
   "source": [
    "# 评估指标\n",
    "def evaluate_clustering(y_true, y_pred):\n",
    "    acc = accuracy_score(y_true, y_pred)\n",
    "    nmi = normalized_mutual_info_score(y_true, y_pred)\n",
    "    ari = adjusted_rand_score(y_true, y_pred)\n",
    "    f_measure = f1_score(y_true, y_pred, average='weighted')\n",
    "    \n",
    "    return {\n",
    "        'Accuracy': acc,\n",
    "        'NMI': nmi,\n",
    "        'ARI': ari,\n",
    "        'F_measure': f_measure\n",
    "    }\n",
    "\n",
    "metrics = evaluate_clustering(y, y_pred)\n",
    "print(\"聚类评估指标:\")\n",
    "for name, value in metrics.items():\n",
    "    print(f\"{name}: {value:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "def visualize_results(X, y_true, y_pred, feature_names=None):\n",
    "    plt.figure(figsize=(15, 6))\n",
    "    \n",
    "    # 真实标签可视化\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y_true, cmap='viridis')\n",
    "    plt.title('True Clusters')\n",
    "    if feature_names:\n",
    "        plt.xlabel(feature_names[0])\n",
    "        plt.ylabel(feature_names[1])\n",
    "    \n",
    "    # 预测标签可视化\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap='viridis')\n",
    "    plt.title('Predicted Clusters')\n",
    "    if feature_names:\n",
    "        plt.xlabel(feature_names[0])\n",
    "        plt.ylabel(feature_names[1])\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "visualize_results(X, y, y_pred, feature_names=['Sepal Length', 'Sepal Width'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、实验结果分析\n",
    "\n",
    "1. 聚类效果评估指标展示\n",
    "2. 真实标签与预测标签的可视化对比\n",
    "3. 不同K值对聚类效果的影响\n",
    "4. 算法收敛速度分析\n",
    "\n",
    "## 五、实验总结\n",
    "\n",
    "1. KMeans算法的优缺点总结\n",
    "2. 实验中遇到的问题及解决方案\n",
    "3. 改进思路"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env0",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
